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Monthly Archives: July 2016

There is a lot of truth and wisdom in the quote, “The perfect is the enemy of the good.” The quote definitely applies when trying to get the most out of data. However, in this context, it should really be “Perfect is the enemy of budget and time.”

Pragmatic data governance protects your budget and timelines against perfectionism. The end goal is, of course, to have all issues resolved, but being able to start governing the data before the end is vital. To do this, data governance has to be built around the concept of managing “imperfect” data and incrementally improving that data.

A key failure of data governance in almost every organization is the wanting to come to agreement before making data available. A far more practical and useful approach is to bring in data and metadata and then identify and manage where issues reside. Here are some typical types of issues:

Differing definitions for attributes—probably the most common issue with reporting

Incomplete data

Erroneous and stale attributes

A caveat: Never assume that differences in data are errors. These differences are usually between multiple functioning applications, and in most cases, the data is suitable for each system and may actually exist for a very good reason. In some cases, immediately fixing differences in data may not be critical.

To manage data issues, cataloging your data is important. This can be done by collecting the metadata and reporting on issues. This catalog then becomes the place for tracking priorities, issues and resolutions. Even modest capability in this area can greatly improve an organization’s use of its already existing data assets.

Remember, the fastest way to perfect data for your use is to figure out how to make the data a little better on an incremental basis.